A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand

Jannik Zgraggen; Matthew Tsao; Mauro Salazar; Maximilian Schiffer; Marco Pavone

doi:10.1109/ITSC.2019.8917521

A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand

Jannik Zgraggen
, Matthew Tsao
, Mauro Salazar
, Maximilian Schiffer
, Marco Pavone

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

12 Citaten (Scopus)

Samenvatting

This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers' travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.

Originele taal-2	Engels
Titel	2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Uitgeverij	Institute of Electrical and Electronics Engineers
Pagina's	1953-1960
Aantal pagina's	8
ISBN van elektronische versie	9781538670248
DOI's	https://doi.org/10.1109/ITSC.2019.8917521
Status	Gepubliceerd - okt. 2019
Extern gepubliceerd	Ja
Evenement	22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019 - Auckland, Nieuw-Zeeland Duur: 27 okt. 2019 → 30 okt. 2019 Congresnummer: 22

Congres

Congres	22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019
Verkorte titel	ITSC 2019
Land/Regio	Nieuw-Zeeland
Stad	Auckland
Periode	27/10/19 → 30/10/19

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@inproceedings{9612ee6fbc714d0a82ef2bf26781ad9a,

title = "A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand",

abstract = "This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers' travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.",

author = "Jannik Zgraggen and Matthew Tsao and Mauro Salazar and Maximilian Schiffer and Marco Pavone",

year = "2019",

month = oct,

doi = "10.1109/ITSC.2019.8917521",

language = "English",

pages = "1953--1960",

booktitle = "2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019, ITSC 2019 ; Conference date: 27-10-2019 Through 30-10-2019",

}

Zgraggen, J, Tsao, M, Salazar, M, Schiffer, M & Pavone, M 2019, A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand. in 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019., 19211343, Institute of Electrical and Electronics Engineers, blz. 1953-1960, 22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019, Auckland, Nieuw-Zeeland, 27/10/19. https://doi.org/10.1109/ITSC.2019.8917521

A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand. / Zgraggen, Jannik; Tsao, Matthew; Salazar, Mauro et al.
2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. Institute of Electrical and Electronics Engineers, 2019. blz. 1953-1960 19211343.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand

AU - Zgraggen, Jannik

AU - Tsao, Matthew

AU - Salazar, Mauro

AU - Schiffer, Maximilian

AU - Pavone, Marco

N1 - Conference code: 22

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N2 - This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers' travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.

AB - This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers' travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers

T2 - 22nd International IEEE Conference on Intelligent Transportation Systems, ITSC 2019

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ER -

A model predictive control scheme for Intermodal Autonomous Mobility-on-Demand

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